Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.
Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai, 200032, People's Republic of China.
J Cancer Res Clin Oncol. 2021 Dec;147(12):3757-3767. doi: 10.1007/s00432-021-03617-3. Epub 2021 Apr 10.
Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient's prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters.
HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction.
Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients.
The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient's prognosis.
微血管侵犯（MVI）是肝细胞癌（HCC）患者早期复发和预后不良的关键决定因素。预测 MVI 状态对治疗策略的决策和患者预后的评估具有重要的临床意义。本研究旨在开发一种基于术前动态对比增强磁共振成像（DCE-MRI）和临床参数的深度学习（DL）模型，以预测 HCC 患者的 MVI 状态和分级。
纳入 2016 年 1 月至 12 月期间经病理证实 MVI 状态的 HCC 患者，并收集这些患者的术前 DCE-MRI。然后，将其随机分为训练集和测试集。建立了一个具有 8 个常规神经网络（CNN）分支的 DL 模型，用于预测 MVI 的存在，并进一步结合临床参数进行更好的预测。
在 601 例 HCC 患者中，376 例患者病理 MVI 阴性，225 例患者 MVI 阳性。为了预测 MVI 的存在，仅基于图像的 DL 模型在测试集中的 AUC 为 0.915，而基于放射组学模型的 AUC 为 0.731。DL 结合临床参数（DLC）模型的预测性能最佳，AUC 为 0.931。对于 MVI 分级分层，DLC 模型的总体准确性为 0.793。生存分析表明，DLC 预测的 MVI 状态与患者的总生存期（OS）和无复发生存期（RFS）较差相关。进一步研究表明，广泛切除边缘的肝切除术有助于改善 DLC 预测的 MVI 阳性患者的 OS 和 RFS。
本研究提出的 DLC 模型可提供一种术前评估 MVI 的非侵入性方法，有助于外科医生做出手术策略决策和评估患者的预后。
